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Covariance selection by thresholding the sample correlation matrix

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  • Jiang, Binyan

Abstract

This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C1logp/n)1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (logp/n)1/2 is shown to be optimal.

Suggested Citation

  • Jiang, Binyan, 2013. "Covariance selection by thresholding the sample correlation matrix," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2492-2498.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:11:p:2492-2498
    DOI: 10.1016/j.spl.2013.07.008
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    References listed on IDEAS

    as
    1. Binyan Jiang & Wei-Liem Loh, 2012. "On the sparsity of signals in a random sample," Biometrika, Biometrika Trust, vol. 99(4), pages 915-928.
    2. Cai, Tony & Liu, Weidong, 2011. "Adaptive Thresholding for Sparse Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 672-684.
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    Cited by:

    1. Binyan Jiang, 2015. "An empirical estimator for the sparsity of a large covariance matrix under multivariate normal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 211-227, April.

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